code: def test_ensemble_kalman_filter_expect_mvn(self):
      state = ekf.EnsembleKalmanFilterState(
          step=0, particles=[1.0], extra=None
      )
  
      with self.assertRaises(ValueError):
          state = ekf.ensemble_kalman_filter_update(
              state,
              observation=[0.0],
              observation_fn=lambda t, p, e: (
                  poisson.Poisson(rate=p),
                  e,
              ),
          )
  
imports: import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.experimental.sequential import ensemble_kalman_filter
  from tensorflow_probability.python.internal import test_combinations
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 1 for calling the EnsembleKalmanFilterTest obj's underlying member method code with example usage: obj.test_ensemble_kalman_filter_expect_mvn() without return types